The da approaches based on encoder-decoder reconstruction typically learn the domain-invariant representation by a shared encoder and maintain the domain-specific representation by a reconstruction loss in the source and target domains. Xavier and Bengio [1]} proposed to use as the encoder the Stacked Denoising Autoencoder (SDA) [2]}. SDA extracts a high-level representation of the inputs (coming from all the domains), which is then passed to a linear classifier - trained on the source domain labeled data - to perform predictions on the target data. A more efficient evolution of this approach is presented in [3]}. Ghifary et al. [4]} introduced the Deep Reconstruction Classification Network (DRCN), a cnn comprising a shared encoder and two branches. The encoder provides the feature representation; the first branch learns to classify source data by exploiting supervision from source labels; the second is a deconvolutional network optimized for unsupervised reconstruction with target data. On the other hand, the authors of [5]} proposed the Domain Separation Network (DSN), comprising two encoders. One is a shared-weight encoder aiming at learning to capture shared representations; the second is a private encoder used for domain-specific components in each domain. More, a shared decoder learns to reconstruct the input samples by exploiting both the private and the shared representation.
